rapidhash - Very fast, high quality, platform-independent

The fastest recommended hash function by SMHasher.

The fastest passing hash in SMHasher3.

rapidhash is wyhash' official successor, with improved speed, quality and compatibility.

Fast
Extremely fast for both short and large inputs.
The fastest hash function passing all tests in SMHasher.
The fastest hash function passing all tests in SMHasher3.
About 6% higher throughput than wyhash according to SMHasher and SMHasher3 reports.

Universal
Optimized for both AMD64 and modern AArch64 systems.
Compatible with gcc, clang, icx and MSVC.
It does not use machine-specific vectorized or cryptographic instruction sets.
Prepared for both C and C++ compilation.

Excellent
Passes all tests in both SMHasher and SMHasher3.
Collision-based study showed a collision probability lower than wyhash and close to ideal.
Outstanding collision ratio when tested with datasets of 16B and 66B keys:

Input Len	Nb Hashes	Expected	Nb Collisions
12	15 Gi	7.0	7
16	15 Gi	7.0	12
24	15 Gi	7.0	7
32	15 Gi	7.0	12
40	15 Gi	7.0	7
48	15 Gi	7.0	7
56	15 Gi	7.0	12
64	15 Gi	7.0	6
256	15 Gi	7.0	4
12	62 Gi	120.1	131
16	62 Gi	120.1	127
24	62 Gi	120.1	126
32	62 Gi	120.1	133
40	62 Gi	120.1	145
48	62 Gi	120.1	123
56	62 Gi	120.1	143
64	62 Gi	120.1	192
256	62 Gi	120.1	181

More results can be found in the collisions folder

Collision-based hash quality study

A perfect hash function distributes its domain uniformly onto the image.
When the domain's cardinality is a multiple of the image's cardinality, each potential output has the same probability of being produced.
A function producing 64-bit hashes should have a $p=1/2^{64}$ of generating each output.

If we compute $n$ hashes, the expected amount of collisions should be the number of unique input pairs times the probability of producing a given hash.
This should be $(n*(n-1))/2 * 1/2^{64}$, or simplified: $(n*(n-1))/2^{65}$.
In the case of hashing $15*2^{30}$ (~16.1B) different keys, we should expect to see $7.03$ collisions.

We present an experiment in which we use rapidhash to hash $68$ datasets of $15*2^{30}$ (15Gi) keys each.
For each dataset, the amount of collisions produced is recorded as measurement.
Ideally, the average among measurements should be $7.03$ and its histogram should approximate a binomial distribution.
We obtained a mean value of $7.72$, just $9.8$% over $7.03$.
The results histogram, depicted below, does resemble a slightly inclined binomial distribution:

Each dataset individual result and the collisions test program can be found in the collisions folder.
The same datasets were hashed using wyhash and its default seed $0$, yielding a higher mean collision value of $8.06$
The provided default seed was used to produce rapidhash results.